Artificial Intelligence Applied in Early Prediction of Lower Limb Fracture Complications.

IF 1.7 Q2 MEDICINE, GENERAL & INTERNAL
Aurelian-Dumitrache Anghele, Virginia Marina, Liliana Dragomir, Cosmina Alina Moscu, Iuliu Fulga, Mihaela Anghele, Cristina-Mihaela Popescu
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引用次数: 0

Abstract

Background: Artificial intelligence has become a valuable tool for diagnosing and detecting postoperative complications early. Through imaging and biochemical markers, clinicians can anticipate the clinical progression of patients and the risk of long-term complications that could impact the quality of life or even be life-threatening. In this context, artificial intelligence is crucial for identifying early signs of complications and enabling clinicians to take preventive measures before problems worsen. Materials and methods: This observational study analyzed medical charts from the electronic archive of the Clinical Emergency Hospital in Galați, Romania, covering a four-year period from 2018 to 2022. A neural network model was developed to analyze various socio-demographic and paraclinical data. Key features included patient demographics, laboratory investigations, and clinical outcomes. Statistical analyses were performed to identify significant risk factors associated with deep venous thrombosis (DVT). Results: The analysis revealed a higher prevalence of female patients (60.78%) compared to male patients, indicating a potential gender-related risk factor for DVT. The incidence of DVT was highest among patients aged 71 to 90 years, affecting 56.86% of individuals in this age group, suggesting that advanced age significantly contributes to the risk of developing DVT. Additionally, among the DVT patients, 15.69% had a body mass index (BMI) greater than 30, categorizing them as obese, which is known to increase the risk of thrombotic events. Furthermore, this study highlighted that the highest frequency of DVT was associated with femur fractures, occurring in 52% of patients with this type of injury. The neural network analysis indicated that elevated levels of direct bilirubin (≥1.5 mg/dL) and prothrombin activity (≤60%) were strong predictors of fracture-related complications, with sensitivity and specificity rates of 78% and 82%, respectively. These findings underscore the importance of monitoring these laboratory markers in at-risk populations for early intervention. Conclusions: This study identified critical risk factors for developing DVT, including advanced age, high BMI, and femur fractures, which necessitate longer recovery periods. Additionally, the findings indicate that elevated direct bilirubin and prothrombin activity play a significant role in predicting DVT development. These results suggest that AI can effectively enhance the anticipation of clinical evolution in patients, aiding in early intervention and management strategies.

人工智能在下肢骨折并发症早期预测中的应用。
背景:人工智能已成为早期诊断和检测术后并发症的重要工具。通过成像和生化标记,临床医生可以预测患者的临床进展以及可能影响生活质量甚至危及生命的长期并发症的风险。在这种情况下,人工智能对于识别并发症的早期征兆并使临床医生在问题恶化之前采取预防措施至关重要。材料与方法这项观察性研究分析了罗马尼亚加拉比临床急诊医院电子档案中的病历,时间跨度为 2018 年至 2022 年,为期四年。我们开发了一个神经网络模型来分析各种社会人口学和准临床数据。主要特征包括患者人口统计学、实验室检查和临床结果。通过统计分析,确定了与深静脉血栓(DVT)相关的重要风险因素。结果显示分析结果显示,与男性患者相比,女性患者的发病率更高(60.78%),这表明深静脉血栓形成的潜在风险因素与性别有关。深静脉血栓的发病率在 71 至 90 岁的患者中最高,该年龄段的患者占 56.86%,这表明高龄是导致深静脉血栓风险的重要因素。此外,在深静脉血栓患者中,15.69%的人体重指数(BMI)大于 30,属于肥胖人群,而众所周知,肥胖会增加血栓事件的风险。此外,这项研究还强调,深静脉血栓形成的最高频率与股骨骨折有关,52%的患者都有这种损伤。神经网络分析表明,直接胆红素水平升高(≥1.5 mg/dL)和凝血酶原活动度升高(≤60%)是骨折相关并发症的有力预测因素,其敏感性和特异性分别为78%和82%。这些发现强调了在高危人群中监测这些实验室指标以进行早期干预的重要性。结论:这项研究确定了发生深静脉血栓的关键风险因素,包括高龄、高体重指数和股骨骨折,这些因素需要更长的恢复期。此外,研究结果表明,直接胆红素和凝血酶原活动度升高在预测深静脉血栓形成方面起着重要作用。这些结果表明,人工智能可有效提高对患者临床演变的预测能力,有助于早期干预和管理策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinics and Practice
Clinics and Practice MEDICINE, GENERAL & INTERNAL-
CiteScore
2.60
自引率
4.30%
发文量
91
审稿时长
10 weeks
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